The Impact of Practical Skills on Improving the Servicemen’s Preparedness to Act in Case of Radiation Contamination of the Area
Bibliographic record
Abstract
The servicemen’s practical skills to respond to threats of chemical, biological, radiological and nuclear attacks, as well as the ability to make effective decisions are necessary for the implementation of effective targeted actions in the face of military threats. The aim of the article is to identify the impact of servicemen’s decision-making skills on their preparedness to act in case of radiation contamination of the area as well as an analysis of the opportunities of skills development in the educational simulation environment. The research employed such empirical methods as: educational experiment, testing, survey, quantitative assessment, and qualitative analysis. The study of causal relationships between servicemen’s decision-making skills under Contaminated Remains Mitigation System CRMS conditions and their preparedness to act under conditions of radiation contamination made it possible to identify a set of decision-making skills that affect high, medium and low servicemen’s preparedness to act under the chemical, biological, radiological, and nuclear (CBRN) attacks. The authors developed and tested a virtual reality training simulator for training decision-making skills in a simulated environment of potential threats using the Zaporizhzhia Nuclear Power Plant (NPP) situation as an example. The results of the assessment of students’ knowledge after the educational experiment showed that simulation training in virtual reality was more effective than training using educational video content. The students of the experimental group (EG) showed a 13.2 points better result (90.6 points) in decision-making accuracy than the students of the control group (CG) (77.4 points).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".